def model_train(img_size,
batch_size,
epochs,
optimizer,
learning_rate,
train_list,
validation_list,
style=2):
print('Style {}.'.format(style))
if style == 1:
input_img = Input(shape=img_size)
#model = Sequential()
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal',
input_shape=img_size)(input_img)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
res_img = model
output_img = merge.Add()([res_img, input_img])
model = Model(input_img, output_img)
#model.load_weights('vdsr_model_edges.h5')
adam = Adam(lr=0.000005)
#sgd = SGD(lr=1e-3, momentum=0.9, decay=1e-4, nesterov=False)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001, nesterov=False)
#model.compile(sgd, loss='mse', metrics=[PSNR, "accuracy"])
model.compile(adam,
loss='mse',
metrics=[ssim, ssim_metric, PSNR, "accuracy"])
model.summary()
else:
input_img = Input(shape=img_size)
model = Conv2D(64, (3, 3),
padding='valid',
kernel_initializer='he_normal')(input_img)
model_0 = Activation('relu')(model)
total_conv = 22 # should be even number
total_conv -= 2 # subtract first and last
residual_block_num = 5 # should be even number
for _ in range(residual_block_num): # residual block
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model_0)
model = Activation('relu')(model)
for _ in range(int(total_conv / residual_block_num) - 1):
model = Conv2D(64, (3, 3),
padding='same',
kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model_0 = add([model, model_0])
model = Conv2D(1, (3, 3),
padding='valid',
kernel_initializer='he_normal')(model)
res_img = model
input_img1 = crop(1, 2, -2)(input_img)
input_img1 = crop(2, 2, -2)(input_img1)
print(input_img.shape)
print(input_img1.shape)
output_img = merge.Add()([res_img, input_img1])
# output_img = res_img
model = Model(input_img, output_img)
# model.load_weights('./vdsr_model_edges.h5')
# adam = Adam(lr=learning_rate)
adam = Adadelta()
# sgd = SGD(lr=1e-7, momentum=0.9, decay=1e-2, nesterov=False)
sgd = SGD(lr=learning_rate,
momentum=0.9,
decay=1e-4,
nesterov=False,
clipnorm=1)
if optimizer == 0:
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR])
else:
model.compile(sgd, loss='mse', metrics=[ssim, ssim_metric, PSNR])
model.summary()
mycallback = MyCallback(model)
timestamp = time.strftime("%m%d-%H%M", time.localtime(time.time()))
csv_logger = callbacks.CSVLogger(
'data/callbacks/training_{}.log'.format(timestamp))
filepath = "./checkpoints/weights-improvement-{epoch:03d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [mycallback, checkpoint, csv_logger]
# print('Loading training data.')
# x = load_images(DATA_PATH)
# print('Loading data label.')
# y = load_images(LABEL_PATH)
# print('Loading validation data.')
# val = load_images(VAL_PATH)
# print('Loading validation label.')
# val_label = load_images(VAL_LABEL_PATH)
# print(x.shape)
# print(y.shape)
# print(val.shape)
# print(val_label.shape)
with open('./model/vdsr_architecture.json', 'w') as f:
f.write(model.to_json())
# datagen = ImageDataGenerator(rotation_range=45,
# zoom_range=0.15,
# horizontal_flip=True,
# vertical_flip=True)
# history = model.fit_generator(datagen.flow(x, y, batch_size=batch_size),
# steps_per_epoch=len(x) // batch_size,
# validation_data=(val, val_label),
# validation_steps=len(val) // batch_size,
# epochs=epochs,
# callbacks=callbacks_list,
# verbose=1,
# shuffle=True,
# workers=256,
# use_multiprocessing=True)
history = model.fit_generator(
image_gen(train_list, batch_size=batch_size),
steps_per_epoch=384400 * (len(train_list)) // batch_size,
# steps_per_epoch=4612800//batch_size,
validation_data=image_gen(validation_list, batch_size=batch_size),
validation_steps=384400 * (len(validation_list)) // batch_size,
epochs=epochs,
workers=1024,
callbacks=callbacks_list,
verbose=1)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model
# plt.plot(history.history['accuracy'])
# plt.plot(history.history['val_accuracy'])
# plt.title('Model accuracy')
# plt.ylabel('Accuracy')
# plt.xlabel('Epoch')
# plt.legend(['Train', 'validation'], loc='upper left')
# # plt.show()
# plt.savefig('accuracy.png')
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('loss.png')
plt.plot(history.history['PSNR'])
plt.plot(history.history['val_PSNR'])
plt.title('Model PSNR')
plt.ylabel('PSNR')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('PSNR.png')
def model_train(img_size, batch_size, epochs, optimizer, learning_rate, train_list, validation_list, style=2):
print('Style {}.'.format(style))
if style == 1:
input_img = Input(shape=img_size)
#model = Sequential()
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', input_shape=img_size)(input_img)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = merge.Add()([res_img, input_img])
model = Model(input_img, output_img)
#model.load_weights('vdsr_model_edges.h5')
adam = Adam(lr=0.000005)
sgd = SGD(lr=0.01, momentum=0.9, decay=0.001, nesterov=False)
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR, "accuracy"])
model.summary()
else:
input_img = Input(shape=img_size)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(input_img)
model = BatchNormalization()(model)
model_0 = Activation('relu')(model)
total_conv = 22 # should be even number
total_conv -= 2 # subtract first and last
residual_block_num = 5 # should be even number
for _ in range(residual_block_num): # residual block
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(model_0)
model = BatchNormalization()(model)
model = Activation('relu')(model)
print(_)
for _ in range(int(total_conv/residual_block_num)-1):
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(model)
model = BatchNormalization()(model)
model = Activation('relu')(model)
model_0 = add([model, model_0])
print(_)
model = Conv2DTranspose(64, (5, 5), padding='valid', kernel_initializer='he_normal', use_bias=False)(model)
model = BatchNormalization()(model)
model = LeakyReLU()(model)
model = Conv2D(1, (5, 5), padding='valid', kernel_initializer='he_normal')(model)
res_img = model
#input_img1 = crop(1,22,-22)(input_img)
#input_img1 = crop(2,22,-22)(input_img1)
print(input_img.shape)
output_img = merge.Add()([res_img, input_img])
# output_img = res_img
model = Model(input_img, output_img)
# model.load_weights('./vdsr_model_edges.h5')
# adam = Adam(lr=learning_rate)
adam = Adadelta()
sgd = SGD(lr=learning_rate, momentum=0.9, decay=1e-4, nesterov=False, clipnorm=1)
if optimizer == 0:
model.compile(adam, loss='mse', metrics=[ssim, ssim_metric, PSNR])
else:
model.compile(sgd, loss='mse', metrics=[ssim, ssim_metric, PSNR])
model.summary()
mycallback = MyCallback(model)
timestamp = time.strftime("%m%d-%H%M", time.localtime(time.time()))
csv_logger = callbacks.CSVLogger('data/callbacks/deconv/training_{}.log'.format(timestamp))
filepath="./checkpoints/deconv/weights-improvement-{epoch:03d}-{PSNR:.2f}-{ssim:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [mycallback, checkpoint, csv_logger]
with open('./model/deconv/vdsr_architecture.json', 'w') as f:
f.write(model.to_json())
history = model.fit_generator(image_gen(train_list, batch_size=batch_size),
steps_per_epoch=(409600//8)*len(train_list) // batch_size,
validation_data=image_gen(validation_list,batch_size=batch_size),
validation_steps=(409600//8)*len(validation_list) // batch_size,
epochs=epochs,
workers=1024,
callbacks=callbacks_list,
verbose=1)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model
# Plot training & validation loss values
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('loss.png')
plt.plot(history.history['PSNR'])
plt.plot(history.history['val_PSNR'])
plt.title('Model PSNR')
plt.ylabel('PSNR')
plt.xlabel('Epoch')
plt.legend(['Train', 'validation'], loc='upper left')
# plt.show()
plt.savefig('PSNR.png')
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same',
kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
# model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5')
adam = Adam(lr=0.00001)
sgd = SGD(lr=1e-5, momentum=0.9, decay=1e-4, nesterov=False)
model.compile(adam, loss='mse', metrics=[PSNR, "accuracy"])
model.summary()
filepath = "./checkpoints/weights-improvement-{epoch:02d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [checkpoint]
model.fit_generator(image_gen(train_list), steps_per_epoch=len(train_list) // BATCH_SIZE, \
validation_data=image_gen(test_list), validation_steps=len(train_list) // BATCH_SIZE, \
epochs=EPOCHS, workers=8, callbacks=callbacks_list)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
# model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5')
adam = Adam(lr=0.00001)
sgd = SGD(lr=1e-5, momentum=0.9, decay=1e-4, nesterov=False)
model.compile(adam, loss='mse', metrics=[PSNR, "accuracy"])
model.summary()
filepath="./checkpoints/weights-improvement-{epoch:02d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [checkpoint]
model.fit_generator(image_gen(train_list), steps_per_epoch=len(train_list) // BATCH_SIZE, \
validation_data=image_gen(test_list), validation_steps=len(train_list) // BATCH_SIZE, \
epochs=EPOCHS, workers=8, callbacks=callbacks_list)
print("Done training!!!")
print("Saving the final model ...")
model.save('vdsr_model.h5') # creates a HDF5 file
del model # deletes the existing model